Claude Opus 4.8 (Adaptive Reasoning, Max Effort) vs GPT-4: The Ultimate Performance & Pricing Comparison

Claude Opus 4.8 is the stronger and cheaper choice for most new developer workloads, based on the available capability and pricing evidence. The Artificial Analysis snapshot gives Claude Opus 4.8 a coding index of 74.3 versus 13.1 for GPT-4, while its intelligence index is 55.7 versus 7 for GPT-4. The same snapshot reports equal latency of 0.3 seconds for both models. Data provided by Artificial Analysis supplies the comparison data used in this article.

The comparison still has an important qualification: GPT-4 is not currently documented with equivalent official detail in the reviewed OpenAI model and pricing pages. OpenAI’s current models documentation does not provide current GPT-4 capability parameters, and its pricing documentation does not list GPT-4. That makes Claude the clearer selection on measured evidence, while GPT-4 carries greater verification and availability risk.

Claude Opus 4.8 offers the better default for developers because it combines materially stronger measured coding and intelligence scores with a lower blended token price. Artificial Analysis reports a coding index of 74.3 for Claude Opus 4.8 and 13.1 for GPT-4. The intelligence index is 55.7 for Claude Opus 4.8 and 7 for GPT-4.

That gap matters most in tasks where the model must understand an existing codebase, maintain constraints across files, diagnose failures, or produce a complete implementation. It does not prove that every prompt will produce a better answer. It does establish a large difference in the available benchmark signal.

Claude also has a clearer current product story. Anthropic describes Claude Opus 4.8 as a model for complex coding, agent workflows, and professional knowledge work in its release announcement. Anthropic’s model overview documents the model identity and supported reasoning controls.

GPT-4 is harder to evaluate as a current procurement target. The reviewed OpenAI pages do not confirm its present API status, stable model identity, context limits, output limits, or current supported modalities. Developers should therefore treat GPT-4 as an existing dependency that requires an environment-specific check, not as a clearly documented new default.

Claude Opus 4.8 is the stronger measured performer for coding and general intelligence tasks, but the available evidence does not establish a complete production latency or reliability profile. Artificial Analysis reports a coding index of 74.3 for Claude Opus 4.8 versus 13.1 for GPT-4. Its intelligence index reports 55.7 versus 7. Those differences are large enough to change engineering decisions for repository work, debugging, planning, and tool-using workflows.

A higher coding score should translate into fewer cases where developers must restate requirements, repair incomplete patches, or manually connect separate implementation steps. It does not guarantee safe autonomous execution. A Reddit user who tested Claude Opus 4.8 across long coding, document analysis, and creative work reported better self-correction and more adaptive answer length, but also observed that multi-step agents could skip explicit steps and reach correct results through messy paths. The report was personal and did not publish a reproducible test set. The Reddit experience report supports a practical caution, not a benchmark conclusion.

The latency comparison is a tie at 0.3 seconds for both models. The data snapshot does not provide median output tokens per second for either model, so it cannot answer which model streams long answers faster. Developers building interactive tools should measure time to first token, output duration, retries, and tool-call overhead in their own stack.

Claude’s reasoning controls add useful operational choice. Anthropic documents effort levels in its Effort documentation, but effort is a behavior signal rather than a strict cost or latency ceiling. A Reddit comment also reports that Adaptive thinking may underestimate hidden complexity in some non-coding tasks. That claim lacks a public controlled test, so teams should validate effort settings against representative workloads.

Claude Opus 4.8 is the cheaper model on every reported token price, but workload design still determines the real cost of a developer workflow. Artificial Analysis reports a blended price of $10 per 1M tokens for Claude Opus 4.8 versus $37.5 for GPT-4. It also reports input prices of $5 versus $30 and output prices of $25 versus $60.

The lower Claude price changes the economics of iterative development. A team can afford more review passes, test-result analysis, and repair attempts before reaching the same token budget. That advantage is especially relevant for coding agents, where one failed tool call can trigger additional context, repeated planning, and another patch.

The cheaper model can still become more expensive in practice if it produces unreliable process traces or requires frequent human correction. The Reddit report describes skipped steps and unverified guesses in multi-step agent tasks. A GitHub issue also collects user reports of verbose, terminology-heavy explanations and instruction drift across longer conversations. Claude Code Issue #77136 is a community report, not a confirmed Anthropic defect.

Anthropic’s official pricing page documents additional prompt caching options for Claude. Those options may improve repeated-context economics, but the data brief does not provide an equivalent GPT-4 price listing from the current OpenAI page. Therefore, the headline cost advantage is clear, while a fully normalized production cost comparison remains unavailable.

Developers should compare total task cost, including retries, review time, tool calls, and failed changes. The available data proves Claude is cheaper per reported token metric. It does not prove that every application will spend fewer dollars end to end.

Claude Opus 4.8 is the recommended default for new developer-facing applications unless an existing GPT-4 dependency has a verified operational reason to remain. The measured coding index is 74.3 for Claude Opus 4.8 and 13.1 for GPT-4, while the blended price is $10 versus $37.5 per 1M tokens. Artificial Analysis provides those values.

Choose Claude Opus 4.8 for repository-aware coding, code review, debugging, technical analysis, and agent workflows that benefit from stronger measured reasoning. Anthropic positions the model for complex coding and agent work in its official announcement. Its documented effort controls can help teams trade answer quality against operational cost, although they are not strict resource limits. Anthropic’s model overview provides the relevant API model information.

Keep GPT-4 only when your application has a tested integration, a known prompt or output contract, and a verified reason that migration would create unacceptable risk. The current OpenAI model catalog does not expose enough GPT-4 detail to support a fresh capability comparison. The current OpenAI pricing catalog does not list GPT-4, so procurement teams should confirm the actual deployment endpoint and billing behavior before committing.

For autonomous workflows, neither model should receive unrestricted trust based on this comparison alone. Claude has stronger measured scores, but available community evidence still reports skipped steps, hidden-complexity misses, verbose responses, and possible instruction drift. Add tests, tool-call validation, approval gates, and repository checks before allowing either model to modify production systems.

Claude Opus 4.8 has substantially stronger available evidence, while GPT-4 has substantial documentation gaps in the reviewed sources. Anthropic provides current model, reasoning, pricing, and lifecycle documentation for Claude Opus 4.8 through its model overview, Effort guide, pricing page, and deprecation page.

The reviewed OpenAI pages do not establish whether GPT-4 remains directly callable, which stable identifier maps to the intended version, or which current limits apply. They also do not provide a current GPT-4 benchmark or documented failure profile. That is evidence of uncertainty, not evidence that GPT-4 fails every task.

The practical conclusion is asymmetric. Claude can be selected from current documented and measured signals. GPT-4 requires a deployment-specific verification step before a fair procurement decision. Teams should confirm endpoint availability, model behavior, rate limits, context handling, and billing in the environment where the application will run.